Project description:Females typically outlive males, a disparity mitigated by castration, yet the molecular underpinnings remain elusive. Our study leverages untargeted metabolomics and RNA sequencing to uncover the pivotal compounds and genes influencing healthy aging post-castration, examining serum, kidney, and liver biospecimens from 12-week and 18-month old castrated male mice and their unaltered counterparts. Behavioral tests and LC-MS/MS metabolomics reveal that castrated males exhibit altered steroid hormones, superior cognitive performance, and higher levels of anti-oxidative compounds like taurine, despite identical diets. Integrated metabolome-transcriptome analysis confirms reduced lipid peroxidation and oxidative stress in female and castrated male mice, suggesting a protective mechanism against aging. Histological examinations post-cisplatin treatment highlight the model’s applicability in studying drug toxicity and reveal varying susceptibility in organ-specific toxicities, underlining the crucial role of sex hormones in physiological defenses. In essence, our castration model unveils a feminized metabolic and transcriptomic intermediary, serving as a robust tool for studying gender-specific aspects of healthy aging and exploring sex hormone-induced differences in diverse biomedical domains.
Project description:The paper "Metabolomic Machine Learning Predictor for Diagnosis and Prognosis of Gastric Cancer" addresses the need for non-invasive diagnostic tools for gastric cancer (GC). Traditional methods like endoscopy are invasive and expensive. The authors conducted a targeted metabolomics analysis of 702 plasma samples to develop machine learning models for GC diagnosis and prognosis. The diagnostic model, using 10 metabolites, achieved a sensitivity of 0.905, outperforming conventional protein marker-based methods. The prognostic model effectively stratified patients into risk groups, surpassing traditional clinical models.
I have successfully reproduced the diagnosis model from the paper. This machine learning-based system differentiates GC patients from non-GC controls using metabolomics data from plasma samples analyzed by liquid chromatography-mass spectrometry (LC-MS). The model focuses on 10 metabolites, including succinate, uridine, lactate, and serotonin. Employing LASSO regression and a random forest classifier, the model achieved an AUROC of 0.967, with a sensitivity of 0.854 and specificity of 0.926. This model significantly outperforms traditional diagnostic methods and underscores the potential of integrating machine learning with metabolomics for early GC detection and treatment.
Project description:Full clinical data for a cohort of 199 individuals with acute coronary syndrome.
Untargeted serum metabolomics using the Metabolon platform for individuals with ACS (n=156).
Serum metabolomics using the Nightingale Health (NMR) platform for individuals with ACS and controls (ACS, n=191; controls, n=961).
Project description:In this study, we performed LC-QTOF-MS-based metabolomics and RNA-seq based transcriptome analysis using seven tissues of M. japonicus.
Project description:In this study, we performed LC-QTOF-MS-based metabolomics and RNA-seq based transcriptome analysis using seven tissues of Magnolia obovata
Project description:In this study, we performed LC-QTOF-MS-based metabolomics and RNA-seq based transcriptome analysis using four tissues of A. japonicum.